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Comment by Randy L. Buckner and Cindy Lustig
A major challenge to developing therapies for Alzheimer's disease is the availability of valid and robust diagnostic markers. Clinical assessment and cognitive testing have traditionally been the gold standard. Over the past decade, there has been an increasing emphasis on two categories of neuroimaging markers—those based on structural measures, and those based on metabolic measures. Greicius and colleagues, in their recent paper in the Proceedings of the National Academy of Sciences (2004), suggest a novel diagnostic marker for Alzheimer's disease, based on functional MRI measures.

Their work is based on the recent discovery of a "default network" that is ubiquitously observed in brain imaging studies of healthy, young participants (Raichle et al., 2001). Default network activity is observed during periods of rest and passive tasks that do not require targeted, effortful processing. Anticipating the work of Greicius and colleagues, it is noteworthy that default network in young adults, which prominently includes regions in posterior cingulate and lateral parietal cortex, overlaps anatomically with those regions showing metabolic differences in Alzheimer's disease measured with FDG PET.

Employing a sophisticated analytic procedure that explores brain activity across networks of regions, Greicius et al. optimized the identification of the default network in elderly individuals with and without the earliest signs of Alzheimer's disease. Sensitivity and specificity of discrimination were 85 percent and 77 percent, respectively. As noted in the news story by Hakon Heimer, these numbers are promising and in the range considered clinically relevant.

Greicius and colleagues' observations are important from the perspectives of both clinical and basic science. First, the demonstrated discrimination between demented and nondemented groups using this functional MRI measure holds promise for developing a novel biomarker of Alzheimer's disease that may complement FDG PET metabolic measures. The relation between the changes in default network activity reported here and the common metabolic changes typically measured using FDG PET requires further exploration, but the possibility that the two are strongly related and that functional MRI measures may provide a complementary assessment in the early stages of dementia is intriguing.

Second, the results of their network analysis suggest a functional link to the medial temporal lobe structures that show early pathology in Alzheimer's disease. This may help to resolve the longstanding puzzle of how the pathological changes in medial temporal regions relate to the metabolic changes in parietal and posterior cingulate cortex as measured by PET. The Greicius et al. data suggest the tentative possibility that they are functionally linked and that posterior cortical changes, particularly within posterior cingulate cortex, may arise from anatomic projections between the medial temporal lobe and these regions.

In addition to the specific findings of their study, a social-scientific milestone was achieved in their paper. The data used for their discoveries were not their own. The data were downloaded from a freely available, online archive of raw functional imaging data of previously published manuscripts. The original authors who collected the data had not conceived of the form of analysis Greicius would later employ (we can speak to this point firsthand as original authors of the data). Thus, as intriguing as the results is also the process by which the discovery was made. The work of Greicius and colleagues directly illustrates the potential of open data sharing.

This article makes the very interesting observation that the “default-mode” network differs in healthy elders and patients with Alzheimer’s disease. It contributes to our understanding of the commonly observed changes in resting cerebral metabolism in Alzheimer’s disease. It also begins to point the way to an imaging approach that can reliably distinguish Alzheimer’s patients from normal, healthy elders.

The article is a strong demonstration of the utility of multivariate approaches such as ICA, PLS, or SSM that attempt to isolate covariance patterns. In contrast to more standard voxel-wise approaches, these multivariate approaches directly measure the relationship between functional changes in various brain areas. They often have increased sensitivity for detecting subtle perturbations that may be associated with disease processes such as early Alzheimer’s disease. Thus, in a study that uses a relatively small number of subjects, this technique provides relatively good separation between network expression (as measured by goodness of fit) in Alzheimer’s patients and controls.

Another very strong feature of multivariate, covariance-based approaches is the ability to forward-apply results from one data set to another. In the current paper, the authors sought to find in the data of another group a default-mode network that they had identified in their own data. This ability to test network results across different data sets provides a very powerful method for validating results and provides an important empirical check of the untestable assumptions that usually enter the assessment of p-values in neuroimaging research.

Multivariate approaches are excellent for looking at inter-individual variability in network expression. Network expression, i.e., the degree to which a subject manifests the network, can typically be summarized with a single parameter (in the present case goodness of fit). Networks can be identified prior to the utilization of other variables of interest in an algorithmic, objective manner. The expression of these networks can then be tested for a relationship with those other variables. Because the information of these variables was not used in the identification of the networks in the first place, any association with a network gives additional credence to its validity. In this case, the authors related degree of network expression to disease state. Others have related network expression to various clinical characteristics or test performance measures that are derived independently from the network analysis. For example, in this case, degree of network expression might be associated with dementia severity. Our group has related differential network expression to differential performance on the activation task.

Thus, in principle, the approach taken here has great promise. There are a few practical limitations to the adaptation of the specific implementation utilized in the current paper to the problem of clinical diagnosis.

First, the approach utilized here relies on within-subject variability. The ICA analysis was performed on each subject’s motor task activation data, as collected over time (i.e., each subject’s 4D image). It would be useful to know whether a similar approach could be taken with more static images, such as PET or SPECT, which are the functional images that are most likely to be ubiquitously available at medical centers. The current approach would require each subject to undergo an fMRI study with a specific cognitive activation. This is a more labor-intensive and rarified procedure.

It is also important to keep in mind that the authors have simply demonstrated that the default-mode network identified in young subjects is replicated in elders, while the “fit” of activation data from AD patients to that seen in young subjects is poorer. From a practical point of view, what the analysis has done is taken 24 to 31 ICA components for each subject and attempted to match them to the default-mode network derived from young subjects. The analysis is in a difficult position of finding the one component for each subject that best matches the default-mode network and then calculating the degree to which it deviates from that network. This raises some important questions. Is there a point where the network is so much altered in Alzheimer’s patients that it cannot be reliably identified by the template, and there is no real default-mode network present anymore? Also, how specific is this deterioration in goodness of fit to Alzheimer’s disease? A gradual disappearance of the default-mode network might also be common to neurodegenerative disorders other than Alzheimer's. Thus, for the direct identification of patients with Alzheimer’s disease, it might be useful in the future to characterize a similar default-mode network in Alzheimer’s disease and then use similar methods to determine whether potential Alzheimer’s patients do or do not match that Alzheimer’s disease network.

All of these issues can easily be addressed in further research. These observations simply strengthen the point that this article brings to the fore the unique power of multivariate approaches to address these difficult problems of characterizing network change across diagnostic states.

In addition to commonly used markers for Alzheimer’s disease that are based on structural and metabolic measures, Greicius et al. (2004) suggested another diagnostic marker, based on functional MRI measures. Their work, as highlighted in this article, creates another significant milestone for reliably distinguishing people with Alzheimer’s disease from normal, healthy elders, by observing the "default-mode" brain network, which is active when the brain is at wakeful rest.

Default network activity is observed during periods of rest and passive tasks that do not require targeted, effortful processing. It was originally identified in positron emission tomography (PET) studies and refers specifically to a set of cortical regions that show deactivation when subjects perform cognitively demanding tasks. These regions include the posterior cingulate cortex and precuneus, the inferior parietal cortices, and the dorsal and ventral areas of the medial frontal cortex. Children who have been traumatized often lack imagination and show little symbolic play, in which the child involves an agent in a game of pretence in order to explore the possible happenings in the real world. This, too, is likely to be due to interruptions across the default network. An experiential activity, such as meditation, in which the person pays attention in a particular and purposeful way, in the present moment, and non-judgmentally, is recommended for reactivating these networks. Reduced ability to control entering and exiting the default network state is positively associated with old age (Fair et al., 2009). Since 2007, the concept of the default-mode network has been criticized as not being useful for understanding brain function, on the grounds of a simpler hypothesis: that a resting brain actually does more processing than a brain doing certain "demanding" tasks, and that there is no special significance to the intrinsic activity of the resting brain (Morcom and Fletcher, 2007).

The novel work of Greicius et al. may open the door to more interesting research. This deactivation pattern may represent an organized mode of brain function whose primary role is to support internally oriented mental processes in human beings, and this spontaneous brain activity may play an important role in shaping functional organization. The default network, which also appears to be closely related to the curious pattern of brain activity that ramps up when we daydream, works differently among patients with depression, autism, schizophrenia, and post-traumatic stress disorder than it does in healthy controls (Raichle and Snyder, 2007). For instance, there are links between the default-mode network and a region involved in motivation and reward-seeking behavior among depressive patients. Better understanding of this brain network activity may bring new insights into the pathological patterns and behavioral consequences of various psychiatric disorders, thus helping to design effective interventions.